{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " iris flowers Data Classification using Python Machine Learning .ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cKPWvhuKo-3"
      },
      "source": [
        "#import dataset UCI Machine Learning Repository \n",
        "import seaborn as sn\n",
        "df=sn.load_dataset('iris')"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UMbLkKGLJUI",
        "outputId": "5846330b-fa6b-4a6c-8a43-5e0b7cdf4cf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5PpLp4oMsYv",
        "outputId": "18cf9909-bda4-4a59-a09e-29a338f0ff61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQrXyYYkLTE6",
        "outputId": "9a8965d4-a930-4d67-c2f6-f19bed4c1716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df['species']"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         setosa\n",
              "1         setosa\n",
              "2         setosa\n",
              "3         setosa\n",
              "4         setosa\n",
              "         ...    \n",
              "145    virginica\n",
              "146    virginica\n",
              "147    virginica\n",
              "148    virginica\n",
              "149    virginica\n",
              "Name: species, Length: 150, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3tQdQmpLhy-",
        "outputId": "da56e98f-2ba4-4f8d-e73e-bcc353cdfeb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df['species'].value_counts()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "versicolor    50\n",
              "setosa        50\n",
              "virginica     50\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itDtspG7MRAr",
        "outputId": "869f49a1-3326-46eb-88df-e5c1c7e411c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df['species'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe47a0ac710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgLwrwe4JTlN",
        "outputId": "cbd2659d-b8af-46bb-e065-df0ef5f0c5fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(1)\n",
        "plt.boxplot([df['sepal_length']])\n",
        "plt.figure(2)\n",
        "plt.boxplot([df['sepal_width']])\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANRElEQVR4nO3dX4xc9XmH8edbDG2xAhi8DRQQRirigqok7og/bYRI3USFInNRXzhSREGpHCOaNO1FRW+SlLtKldoQS1gWaRrUhEaxAqWVQUTKRbgoSGNjCA1EcgjEdkkYwLXLH5W6fXvhSTWMZnfPrGd37R/PR1rtmXN+zHlveBjOnplJVSFJOv39wmoPIEmaDYMuSY0w6JLUCIMuSY0w6JLUiDWrdeL169fXhg0bVuv0knRa2rt372tVNTfp2KoFfcOGDfT7/dU6vSSdlpK8PN+xTpdckvxpkn9L8lySB5P80tjxX0zyzSQHkjyVZMPJjSxJmtaiQU9yMfBZoFdVvw6cAWwdW/Yp4EhV/RrwN8BfzXpQSdLCuv5RdA3wy0nWAGcD/z52/Fbga8Pt3cCmJJnNiJKkLhYNelUdBv4a+AnwCnC0qh4fW3YxcHC4/jhwFLhg/LmSbEvST9IfDAYnO7skaUSXSy7rOPEK/HLgV4G1ST65lJNV1a6q6lVVb25u4h9pJUlL1OWSy+8CP66qQVX9N/Bt4LfG1hwGLgUYXpY5F3h9loNKkhbWJeg/Aa5Lcvbwuvgm4PmxNY8Afzjc3gJ8t/wYR0laUV2uoT/FiT907gO+P/xndiW5J8nm4bKvABckOQD8GXD3Ms0rSZpHVuuFdK/XK99YpJWwUjdc+T+lWglJ9lZVb9KxVXunqLRSlhLaJAZapx0/nEuSGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGrFo0JNcmWT/yM+xJJ8bW3NjkqMjaz6/fCNLkiZZ9CvoquqHwIcAkpwBHAYemrD0iaq6ZbbjSZK6mvaSyybgR1X18nIMI0laummDvhV4cJ5j1yd5JsmjSa6atCDJtiT9JP3BYDDlqSVJC+kc9CRnAZuBb004vA+4rKquBr4MPDzpOapqV1X1qqo3Nze3lHklSfOY5hX6TcC+qvrZ+IGqOlZVbw639wBnJlk/oxklSR1ME/RPMM/lliQXJslw+5rh875+8uNJkrpa9C4XgCRrgY8Bnx7Ztx2gqnYCW4A7kxwH3gG2VlXNflxJ0nw6Bb2q3gIuGNu3c2R7B7BjtqNJkqbhO0UlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqRGLBj3JlUn2j/wcS/K5sTVJcm+SA0meTbJx+UaWJE2y6JdEV9UPgQ8BJDkDOAw8NLbsJuCK4c+1wH3D35KkFTLtJZdNwI+q6uWx/bcCD9QJTwLnJbloJhNKkjqZNuhbgQcn7L8YODjy+NBw33sk2Zakn6Q/GAymPLUkaSGdg57kLGAz8K2lnqyqdlVVr6p6c3NzS30aSdIE07xCvwnYV1U/m3DsMHDpyONLhvskSStkmqB/gsmXWwAeAW4b3u1yHXC0ql456ekkSZ0tepcLQJK1wMeAT4/s2w5QVTuBPcDNwAHgbeCOmU8qSVpQp6BX1VvABWP7do5sF3DXbEeTJE3Dd4pKUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1olPQk5yXZHeSF5I8n+T6seM3JjmaZP/w5/PLM64kaT6dvlMU+BLwWFVtSXIWcPaENU9U1S2zG02SNI1Fg57kXOAG4HaAqnoXeHd5x5IkTavLJZfLgQHw1SRPJ7k/ydoJ665P8kySR5NcNemJkmxL0k/SHwwGJzO3JGlMl6CvATYC91XVh4G3gLvH1uwDLquqq4EvAw9PeqKq2lVVvarqzc3NncTYkqRxXYJ+CDhUVU8NH+/mROD/X1Udq6o3h9t7gDOTrJ/ppJKkBS0a9Kr6KXAwyZXDXZuAH4yuSXJhkgy3rxk+7+sznlWStICud7l8Bvj68A6XF4E7kmwHqKqdwBbgziTHgXeArVVVyzGwJGmyTkGvqv1Ab2z3zpHjO4AdM5xLkjSlrq/QpVPC+eefz5EjR1bkXMOriMtm3bp1vPHGG8t6Dr2/GHSdVo4cOUIrV/OW+z8Yev/xs1wkqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa0SnoSc5LsjvJC0meT3L92PEkuTfJgSTPJtm4PONKkubT9RuLvgQ8VlVbhl8UffbY8ZuAK4Y/1wL3DX9LklbIoq/Qk5wL3AB8BaCq3q2q/xhbdivwQJ3wJHBekotmPq0kaV5dLrlcDgyAryZ5Osn9SdaOrbkYODjy+NBwnyRphXQJ+hpgI3BfVX0YeAu4eyknS7ItST9JfzAYLOUpJEnz6BL0Q8Chqnpq+Hg3JwI/6jBw6cjjS4b73qOqdlVVr6p6c3NzS5lXkjSPRYNeVT8FDia5crhrE/CDsWWPALcN73a5DjhaVa/MdlRJ0kK63uXyGeDrwztcXgTuSLIdoKp2AnuAm4EDwNvAHcswqyRpAZ2CXlX7gd7Y7p0jxwu4a4ZzSZKm5DtFJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGtH1PnTplFBfOAe+eO5qjzET9YVzVnsENcag67SSvzzGibc9nP6SUF9c7SnUEi+5SFIjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjOn2WS5KXgP8E/gc4XlW9seM3Av8E/Hi469tVdc/sxpQkLWaaD+f6aFW9tsDxJ6rqlpMdSJK0NF5ykaRGdA16AY8n2Ztk2zxrrk/yTJJHk1w1aUGSbUn6SfqDwWBJA0uSJut6yeUjVXU4ya8A30nyQlV9b+T4PuCyqnozyc3Aw8AV409SVbuAXQC9Xq+ND7WWpFNEp1foVXV4+PtV4CHgmrHjx6rqzeH2HuDMJOtnPKskaQGLBj3J2iQf+Pk28HHgubE1FybJcPua4fO+PvtxJUnz6XLJ5YPAQ8NerwG+UVWPJdkOUFU7gS3AnUmOA+8AW6uV7wmTpNPEokGvqheBqyfs3zmyvQPYMdvRJEnT8LZFSWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRnT9kmjplDH89qzT3rp161Z7BDXGoOu0slLfbJhkxc4lzUqnSy5JXkry/ST7k/QnHE+Se5McSPJsko2zH1WStJBpXqF/tKpem+fYTcAVw59rgfuGvyVJK2RWfxS9FXigTngSOC/JRTN6bklSB12DXsDjSfYm2Tbh+MXAwZHHh4b73iPJtiT9JP3BYDD9tJKkeXUN+keqaiMnLq3cleSGpZysqnZVVa+qenNzc0t5CknSPDoFvaoOD3+/CjwEXDO25DBw6cjjS4b7JEkrZNGgJ1mb5AM/3wY+Djw3tuwR4Lbh3S7XAUer6pWZTytJmleXu1w+CDw0fDPHGuAbVfVYku0AVbUT2APcDBwA3gbuWJ5xJUnzWTToVfUicPWE/TtHtgu4a7ajSZKm4We5SFIjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjOgc9yRlJnk7yLxOO3Z5kkGT/8OePZjumJGkxi35J9Ig/AZ4Hzpnn+Der6o9PfiRJ0lJ0eoWe5BLg94H7l3ccSdJSdb3k8rfAnwP/u8CaP0jybJLdSS6dtCDJtiT9JP3BYDDtrJKkBSwa9CS3AK9W1d4Flv0zsKGqfgP4DvC1SYuqaldV9aqqNzc3t6SBJUmTdXmF/tvA5iQvAf8I/E6SfxhdUFWvV9V/DR/eD/zmTKeUJC1q0aBX1V9U1SVVtQHYCny3qj45uibJRSMPN3Pij6eSpBU0zV0u75HkHqBfVY8An02yGTgOvAHcPpvxJEldpapW5cS9Xq/6/f6qnFtaTBJW698NaSFJ9lZVb9Ix3ykqSY0w6JLUCIMuSY0w6JLUCIMuSY1Y8m2L0ukiyYr8c94Vo9Vm0NU8Q6v3Cy+5SFIjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNWLVPg89yQB4eVVOLi1uPfDaag8hTXBZVU38UuZVC7p0KkvSn+9LBKRTlZdcJKkRBl2SGmHQpcl2rfYA0rS8hi5JjfAVuiQ1wqBLUiMMujQiyd8leTXJc6s9izQtgy69198Dv7faQ0hLYdClEVX1PeCN1Z5DWgqDLkmNMOiS1AiDLkmNMOiS1AiDLo1I8iDwr8CVSQ4l+dRqzyR15Vv/JakRvkKXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEb8H1oGO7EonCb5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPXNJ9e0J92k",
        "outputId": "938d05e4-272c-47b2-d386-b341a28dee9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "df.plot(kind ='density',subplots = True, layout =(3,3),sharex = False)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fe479f87c18>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe47a041940>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe479fe2ef0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe479f3b198>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe479eea400>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe479e9b668>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe479e4b8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe479e85278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe479e852e8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z79FvY6KCFn",
        "outputId": "b8d7f2e6-d1b9-4ae5-f5a4-aa49b0deca7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df.plot(kind ='box',subplots = True, layout =(2,5),sharex = False)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length       AxesSubplot(0.125,0.536818;0.133621x0.343182)\n",
              "sepal_width     AxesSubplot(0.285345,0.536818;0.133621x0.343182)\n",
              "petal_length     AxesSubplot(0.44569,0.536818;0.133621x0.343182)\n",
              "petal_width     AxesSubplot(0.606034,0.536818;0.133621x0.343182)\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgdZYuYZJ2tw",
        "outputId": "d9c7cfe0-e395-447b-eea3-59ee72e9c471",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "df.hist()\n",
        "plt.show()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QshYcPbhM2r_",
        "outputId": "882d403c-3216-4eb4-b1a2-2a9f2d0a437e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "sn.pairplot(df)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fe479b47908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dcrkI3AO1va",
        "outputId": "2c0de47a-952d-4197-a2b9-073f7a2ec677",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "sn.pairplot(df, hue='species')"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fe479ada128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 804.75x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoE9S6okNl4H"
      },
      "source": [
        "df.species=df.species.map({'setosa':1,'versicolor':2,'virginica':3})"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KldqX6jQbR2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gY_bE2kkRHVj"
      },
      "source": [
        "X=df.drop(['species'],axis='columns')\n",
        "Y=df.species"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dPJQqK0RwM8",
        "outputId": "f1301665-663e-4b82-b621-f4fb144d86a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "X"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98bYwjMqRyRR",
        "outputId": "3e8c5b81-e1b7-4859-b90f-841a39aeb7b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "Y"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "145    3\n",
              "146    3\n",
              "147    3\n",
              "148    3\n",
              "149    3\n",
              "Name: species, Length: 150, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6gLDIGPR0ER",
        "outputId": "f04fac5e-8447-4752-acd2-bb7171314043",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(X)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2e6rLQIR9so",
        "outputId": "24cb39fb-e474-4fb6-8e9e-a7084c2fa2ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(Y)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmEsZ7KnS1CR"
      },
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2)"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hx80tUT_Tqgx",
        "outputId": "5f33534a-7258-4843-b472-7ea2de540a82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "X_train"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>6.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "75            6.6          3.0           4.4          1.4\n",
              "98            5.1          2.5           3.0          1.1\n",
              "39            5.1          3.4           1.5          0.2\n",
              "22            4.6          3.6           1.0          0.2\n",
              "14            5.8          4.0           1.2          0.2\n",
              "..            ...          ...           ...          ...\n",
              "46            5.1          3.8           1.6          0.2\n",
              "141           6.9          3.1           5.1          2.3\n",
              "34            4.9          3.1           1.5          0.2\n",
              "149           5.9          3.0           5.1          1.8\n",
              "139           6.9          3.1           5.4          2.1\n",
              "\n",
              "[120 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rg505R1NTtT5",
        "outputId": "e78342b4-9209-447f-a42a-81d834887670",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        }
      },
      "source": [
        "X_test"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>4.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>7.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "63            6.1          2.9           4.7          1.4\n",
              "131           7.9          3.8           6.4          2.0\n",
              "10            5.4          3.7           1.5          0.2\n",
              "69            5.6          2.5           3.9          1.1\n",
              "42            4.4          3.2           1.3          0.2\n",
              "100           6.3          3.3           6.0          2.5\n",
              "50            7.0          3.2           4.7          1.4\n",
              "28            5.2          3.4           1.4          0.2\n",
              "81            5.5          2.4           3.7          1.0\n",
              "5             5.4          3.9           1.7          0.4\n",
              "77            6.7          3.0           5.0          1.7\n",
              "15            5.7          4.4           1.5          0.4\n",
              "56            6.3          3.3           4.7          1.6\n",
              "130           7.4          2.8           6.1          1.9\n",
              "114           5.8          2.8           5.1          2.4\n",
              "117           7.7          3.8           6.7          2.2\n",
              "134           6.1          2.6           5.6          1.4\n",
              "62            6.0          2.2           4.0          1.0\n",
              "102           7.1          3.0           5.9          2.1\n",
              "17            5.1          3.5           1.4          0.3\n",
              "88            5.6          3.0           4.1          1.3\n",
              "145           6.7          3.0           5.2          2.3\n",
              "48            5.3          3.7           1.5          0.2\n",
              "110           6.5          3.2           5.1          2.0\n",
              "136           6.3          3.4           5.6          2.4\n",
              "146           6.3          2.5           5.0          1.9\n",
              "2             4.7          3.2           1.3          0.2\n",
              "92            5.8          2.6           4.0          1.2\n",
              "37            4.9          3.6           1.4          0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OT-NCoYTyXs",
        "outputId": "70073310-52e6-492d-c9af-4d28b23098e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "Y_train"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75     2\n",
              "98     2\n",
              "39     1\n",
              "22     1\n",
              "14     1\n",
              "      ..\n",
              "46     1\n",
              "141    3\n",
              "34     1\n",
              "149    3\n",
              "139    3\n",
              "Name: species, Length: 120, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9bjtOdaT0Pm",
        "outputId": "a2748429-4979-4271-81f9-19dd6d35e6b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "Y_test"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "63     2\n",
              "131    3\n",
              "10     1\n",
              "69     2\n",
              "42     1\n",
              "100    3\n",
              "50     2\n",
              "28     1\n",
              "81     2\n",
              "5      1\n",
              "77     2\n",
              "15     1\n",
              "56     2\n",
              "130    3\n",
              "114    3\n",
              "117    3\n",
              "134    3\n",
              "62     2\n",
              "102    3\n",
              "17     1\n",
              "88     2\n",
              "145    3\n",
              "48     1\n",
              "110    3\n",
              "136    3\n",
              "146    3\n",
              "2      1\n",
              "92     2\n",
              "37     1\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUvDk653T8g_"
      },
      "source": [
        "#Support vector machine\n",
        "from sklearn.svm import SVC\n",
        "model = SVC()"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SBXW-MOUl87",
        "outputId": "27d9a791-27b7-4052-e688-e1f14c44cba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trMijDZjUzp8",
        "outputId": "dfb60bb5-0703-4018-b390-2b57c618d5bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.score(X_test,Y_test)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNWDXcVEVamu",
        "outputId": "a25d39d4-084f-4a03-9323-5b7f4aa47d28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.predict([[6,3,5,2]])"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9EuHqUvVzoF",
        "outputId": "6af2731a-3ccc-4924-f7ca-0a4a625784a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.predict([[12,2,3,1]])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEbohjGJW_-u"
      },
      "source": [
        "catagory = ['Iris-Satosa','Iris-Versicolor','Iris-Virginica']"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9SRZRqaXHfx"
      },
      "source": [
        "data = 5.7,3,4.2,1.1\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnZKypoEXLNo",
        "outputId": "82946fae-1c5c-481f-b8c7-154b252b9d14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "data_array = np.array([data])\n",
        "data_array"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.7, 3. , 4.2, 1.1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSm_q1_JXZYI"
      },
      "source": [
        "predic = model.predict(data_array)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwrS1LWrYfij",
        "outputId": "f48f353d-49ae-44ca-9b55-168d1638920e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(catagory[int(predic[0])])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iris-Virginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGGVrs5DYqjy",
        "outputId": "be19d833-602c-47dd-d635-67d64287eefc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.predict([[6,3,5,2]])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5K8JQ6lAvts"
      },
      "source": [
        "predic = model.predict(data_array)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8R2hBq5JHeed",
        "outputId": "25276408-a6d9-4c17-fbe8-c46743dfed0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(catagory[int(predic)])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iris-Virginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bsy6MSTbHoCm",
        "outputId": "5e306e82-06c8-4c8f-e483-fe3f7e87079d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.predict([[12,2,3,1]])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DO6LFmFDIJrq"
      },
      "source": [
        "#Logistic regression Model\n",
        "from sklearn.linear_model import LogisticRegression "
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JUGXIkeIYkw"
      },
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, Y_train)\n",
        "prediction = model.predict(X_test)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGdxD5c1IoXJ",
        "outputId": "3ec0ba64-3bb3-4a80-e2b7-7ebb1e53b256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(prediction,Y_test))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWZ4Npn7JBVE",
        "outputId": "037a73df-9116-48cc-a7d3-afb9f4941154",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.predict([[6,3,5,2]])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H06Mp8ahK08I"
      },
      "source": [
        "# Decision Tree Classifier Model\n",
        "from sklearn import tree"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zppMhW6oK6Hy",
        "outputId": "b2bbce76-495d-4e79-c127-bcab780a64d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "model = tree.DecisionTreeClassifier()\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQJ15Ad8LWPR"
      },
      "source": [
        "prediction = model.predict(X_test)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8zjHNCwLfJX",
        "outputId": "13682a4c-fa30-4a7b-943f-daf16e784d18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(prediction,Y_test)*100)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPFFQsvuLwZO",
        "outputId": "5f5e6759-fdee-4b68-e303-4b3d76be8d3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#KNeighbors Classifier Model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_model = KNeighborsClassifier(n_neighbors=10)\n",
        "knn_model.fit(X_train, Y_train)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikg2TrdGMUnt"
      },
      "source": [
        "prediction_knn = knn_model.predict(X_test)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHGzZ-uoMb5T"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_knn = accuracy_score(Y_test, prediction_knn)*100"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpiZ-BEdM6S4",
        "outputId": "718bbf66-ce18-46ed-861d-5bab83d7e867",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy_knn"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXfD3LwCN1OX"
      },
      "source": [
        "catagory = ['Iris-Satosa','Iris-Versicolor','Iris-Virginica']"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1jREp1hNLvJ"
      },
      "source": [
        "data = 5.7,3,4.2,1.1"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4hP76z8NO4P",
        "outputId": "8ebe22f8-1722-4851-8eb3-b3a474f387b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "data_array = np.array([data])\n",
        "data_array"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.7, 3. , 4.2, 1.1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eCRxBvKNUJq"
      },
      "source": [
        "predic = model.predict(data_array)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH4u5fAXNZCZ",
        "outputId": "b9c2fbaa-2809-44fe-b669-7dd2c6cd1c0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(catagory[int(predic[0])])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iris-Virginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh70mKQiNc1J"
      },
      "source": [
        "\n",
        "predic = knn_model.predict(data_array)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylPkhpssNgJn",
        "outputId": "4145504c-4539-49cd-a120-d47f4c368824",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(catagory[int(predic)])"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iris-Virginica\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}